Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.1/3265
Título: Detection and Identification of extra virgin olive oil adulteration by GC-MS combined with chemometrics
Autor: Yang, Yang
Ferro, Miguel Duarte
Cavaco, Isabel Maria Palma Antunes
Liang, Yizeng
Palavras-chave: Olive oil
Adulteration
Univariate analysis
Multivariate analysis
PLS-LDA
Monte Carlo tree
Data: 2013
Editora: American Chemical Society
Citação: Yang, Yang; Ferro, Miguel Duarte; Cavaco, Isabel; Liang, Yizeng. Detection and Identification of Extra Virgin Olive Oil Adulteration by GC-MS Combined with Chemometrics, Journal of Agricultural and Food Chemistry, 61, 15, 3693-3702, 2013.
Resumo: In this study, an analytical method for the detection and identification of extra virgin olive oil adulteration with four types of oils (corn, peanut, rapeseed, and sunflower oils) was proposed. The variables under evaluation included 22 fatty acids and 6 other significant parameters (the ratio of linoleic/linolenic acid, oleic/linoleic acid, total saturated fatty acids (SFAs), polyunsaturated fatty acids (PUFAs), monounsaturated fatty acids (MUFAs), MUFAs/PUFAs). Univariate analyses followed by multivariate analyses were applied to the adulteration investigation. As a result, the univariate analyses demonstrated that higher contents of eicosanoic acid, docosanoic acid, tetracosanoic acid, and SFAs were the peculiarities of peanut adulteration and higher levels of linolenic acid, 11-eicosenoic acid, erucic acid, and nervonic acid the characteristics of rapeseed adulteration. Then, PLSLDA made the detection of adulteration effective with a 1% detection limit and 90% prediction ability; a Monte Carlo tree identified the type of adulteration with 85% prediction ability.
Peer review: yes
URI: http://hdl.handle.net/10400.1/3265
DOI: http://dx.doi.org/10.1021/jf4000538
ISSN: 0021-8561
Aparece nas colecções:FCT2-Artigos (em revistas ou actas indexadas)

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